BUG?: value_counts(normalize=True) normalizes over all observations including NaN.

[nickeubank]

By default, value_counts ignores missing values. To get them displayed, one must add the option dropna=False. But when one normalizes, they are entering into the denominator even when dropna=True.

Code Sample, a copy-pastable example if possible

s = pd.Series([1,2,3,np.nan, np.nan, np.nan])
s.value_counts(normalize=True)

0.1667
0.1667
0.1667

Expected Output

0.333
0.333
0.333

output of pd.show_versions()

pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.17.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8

[jreback]

yeh, looks like its not respecting dropna

In [3]: s.value_counts(normalize=True,dropna=False)
Out[3]: 
NaN     0.500000
 3.0    0.166667
 2.0    0.166667
 1.0    0.166667
dtype: float64

In [4]: s.value_counts(normalize=True,dropna=True)
Out[4]: 
3.0    0.166667
2.0    0.166667
1.0    0.166667
dtype: float64